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Artificial intelligence−driven materials science: History, challenges, and future
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Fengxiang ZHOU1, 2, Yanglili ZHOU1, 2, Ziwei ZHAO1, 2, *, Weihua WANG1
Science & Technology Review | 2026, 44(14) : 69 - 79
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Science & Technology Review | 2026, 44(14): 69-79
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Artificial intelligence−driven materials science: History, challenges, and future
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Fengxiang ZHOU1, 2, Yanglili ZHOU1, 2, Ziwei ZHAO1, 2, *, Weihua WANG1
Affiliations
  • 1Dongguan Institute of Materials Science and Technology, Chinese Academy of Sciences, Dongguan 523830, China
  • 2Songshan Lake Materials Laboratory, Dongguan 523830, China
Published: 2026-07-28 doi: 10.3981/j.issn.1000-7857.2026.02.00035
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In response to the long development cycles and heavy reliance on empirical expertise in materials research, this study elucidates the evolutionary logic underlying the transformation of materials science toward a fourth paradigm of "intelligent autonomous creation" enabled by artificial intelligence (AI). It further analyzes the mechanisms by which cognitive capabilities and decision−making authority are transferred from human researchers to intelligent systems. The paper also outlines the multidimensional pathways through which AI enables materials innovation across three major dimensions: materials design, synthesis planning, and autonomous experimental systems. To address limitations such as data silos, limited physical interpretability, and heavy dependence on upfront computational resources, this work argues that embedding physical mechanisms into statistical models is essential to overcome current capability boundaries. Looking ahead, the deep integration of multimodal foundation models with digital twin technologies is expected to establish an intelligent management framework that covers the entire materials lifecycle. This integration is likely to usher materials research into a self−evolving era characterized by algorithm−driven processes and close human–machine collaboration.

materials science  /  artificial intelligence  /  paradigm shift
Fengxiang ZHOU, Yanglili ZHOU, Ziwei ZHAO, Weihua WANG. Artificial intelligence−driven materials science: History, challenges, and future[J]. Science & Technology Review, 2026 , 44 (14) : 69 -79 . DOI: 10.3981/j.issn.1000-7857.2026.02.00035
Year 2026 volume 44 Issue 14
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Article Info
doi: 10.3981/j.issn.1000-7857.2026.02.00035
  • Receive Date:2026-02-10
  • Online Date:2026-08-19
  • Published:2026-07-28
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  • Received:2026-02-10
  • Revised:2026-04-07
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    1Dongguan Institute of Materials Science and Technology, Chinese Academy of Sciences, Dongguan 523830, China
    2Songshan Lake Materials Laboratory, Dongguan 523830, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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